In this section, we introduce our method via three subsections.
In our study, we focus on two crucial semantic labels when describing human faces, age and gender, and generate images directly from these labels, rather than editing randomly generated images.
To do this, we take advantage of the closed-form factorisation algorithm proposed by[1]} to project the latent vectors of our latent space into the scaling factors of the linear space and then use a pre-trained classifier which can output a continuous outcome to classify the randomly generated images according to their pre-defined semantic rules. This approach classifies the corresponding scaling factors at the same time with a different value range.
As a next step to achieve better diversity of the generated images, we propose to uniformly sample the scaling factors in each channel within the corresponding value range.
In the following subsections, we will elaborate on the methodology.
